Precision Medicine Market Trends

Growth Drivers

• Growing investments in R&D activities - Pharma companies and R&D stakeholders have identified the value of pioneering innovative therapies and capitalizing on the commercialization window under patent protection. Consequently, there is a positive correlation between the investment level and the ROI perceived. Furthermore, the maturity, size, and strategy of the company such as the expenditure on clinical trials among pre-clinical companies and on the development of CDx among companies without a CDx program influence the pharma stakeholders.
  
  Collectively, investments in research tools including genomics, cellomics, proteomics, liquid biopsy, microscopy, and imaging is booming at a rapid rate. Omics-based research tools have enabled pharmaceutical companies to capture a high volume of data per sample, at higher sensitivity and resolution. Among the above mentioned research tool categories, genomics has gained the highest traction, investment, and ROI ratings.
  
  The success of the Human Genome Project has driven prominent funding in genomic tools over the past few years.
  
  While still vital parts of the R&D process, the level of funding in cellomics and proteomics is relatively lower than in genomics, due in part to the rising molecular and biological complexity of these analytes compared to nucleic acids. In September 2022, the CDC announced an investment of USD 90.0 million in the Pathogen Genomics Centers of Excellence (PGCoE) network to pilot and implement genomics technologies in the United States public health system.
  
  Additionally, the WHO in February 2024 announced a funding of USD 4.0 million for a catalytic grant fund catering to organizations working in pathogen genomic surveillance in low- and middle-income countries.
• Groundbreaking developments in biomarker-guided therapies- Modern medical research is devoted to pioneering personalized pharmacotherapies and understanding molecular phenotypes in diseases. Within this context, biomarkers-guided therapies provide useful information about the molecular etiology, type, and stage of a disease. A biomarker is quantitatively measured and includes a wide array of biological components including DNA, mRNA, microRNA, small interfering RNA, proteoglycans, proteins, lipids, and sphingolipids. These are detectable in samples of blood, tissues, and urine.
  
  The current landscape of next-generation sequencing (NGS) has promoted the detection of gene alterations, particularly gene mutations, copy number changes, and rearrangements. This has fostered efforts towards precision medicine to render therapies in accordance with genetic alterations. Its proof-of-concept was initiated from the imatinib success for chronic myelogenous leukemia (CML) patients harboring the BCR-ABL translocation. Subsequently, drugs targeting the ALK, EGFR, ROS1, MET-mutant lung cancer, gastric cancer, HER2-overexpression breast cancer, and BRAF V600E mutant melanoma improved patient prognosis and proliferated innovations in precision medicine. 
  
  The rampant exploration of new drug trial designs has contributed to evaluating and innovating treatments using precision medicine. In February 2024, Mindera Health entered into a partnership with Liviniti to pilot a program using Mind.Px and enhance the clinical outcome of psoriasis.

Challenges

• High cost- The high expense of precision medicine is attributed to the extensive collection of information required, despite its potential for tailoring therapies to individual needs. Gathering and examining a substantial volume of data is not only expensive but also requires significant labor and technical expertise. Data anonymization and its security are significant concerns thereby, restricting the precision medicine market growth.
• Impact of turnaround time - The minimum turnaround time for data analysis is 26 hours, which is still considered slow for making choices in acute care settings. Calculating the validity of such data is equally challenging. Rapid capacity building is crucial, particularly in terms of training healthcare professionals and ensuring the availability of advanced artificial intelligence (AI), machine learning (ML), and laboratory equipment. Hence, this slows down the need for opting for precision medicine over traditional treatment methods.